Visual Modeling of Data using Convolutional Neural Networks
Moulana Mohammed1, M. Venkata Sai Sowmya2, Y. Akhila3, B. Naga Megana4
1Moulana Mohammed*, CSE Department, Koneru Lakshmaiah Educational Foundation, Guntur, India.
2M. Sowmya, CSE Department, Koneru Lakshmaiah Educational Foundation, Guntur, India.
3Y. Akhila, CSE Department, Koneru Lakshmaiah Educational Foundation, Guntur, India.
4B. Naga Megana, CSE Department, Koneru Lakshmaiah Educational Foundation, Guntur, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 4938-4942 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2084109119/2019©BEIESP | DOI: 10.35940/ijeat.A2084.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artificial Intelligence has been showing monumental growth in filling the gap between the capabilities of humans and machines. Researchers and scientists work on many aspects to make new things happen. Computer Vision is one of them. To make the system to visualize, neural networks are used. Some of the well-known Neural Networks include CNN, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN) and so on. Among them, CNN is the correct choice for computer vision because they learn relevant features from an image or video similar to the human brain. In this paper, the dataset used is CIFAR-10 (Canadian Institute for Advanced Research) which contains 60,000 images in the size of 32×32. Those images are divided into 10 different classes which contains both training and testing images. The training images are 50,000 and testing images are 10,000. The ten different classes contain airplanes, automobiles, birds, cat, ship, truck, deer, dog, frog and horse images. This paper was mainly concentrated on improving performance using normalization layers and comparing the accuracy achieved using different activation functions like ReLU and Tanh.
Keywords: CNN, Computer Vision, Normalization layer.